基於強化清晰邊緣預測之有效總變異影像去模糊化方法

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姓名

洪宗湧(Tsung-yung Hung) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於強化清晰邊緣預測之有效總變異影像去模糊化方法
(An Effective Total Variation Image Deblurring Method Based on Enhanced Sharp Edge Prediction)

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摘要(中)

以手持相機拍攝影像，即時在正確的相機設定情況下，獲取令人滿意的照片依舊是一項挑戰，換言之，令人感到失望的影像通常都是由於影像模糊，在實際上，造成影像模糊一般都是發生在曝光時間內相機不經意的晃動，以導致於遺失重要的資訊與產生雜訊。
　　在傳統去模糊化方法中，通常會給予假設，分別為：影像在空間/頻率域中的限制，或針對相機移動的軌跡限定參數模型，以解決ill-posed問題。因此現今去模糊化方法的議題在於，如何降低因影像的雜訊與估計軌跡的錯誤所造成的波紋干擾，與提升運算效能。在本論文中，我們提出一個新穎的去模糊化方法，從單一張的已知模糊影像，去模糊化以獲取高品質的影像。本論文的主要目的是，有效的抑止波紋的干擾；並且，相對於傳統的疊代式之去模糊化方法，加速其模糊軌跡的估計與清晰影像的還原之收斂。
　　首先採用一些基本的影像前處理與邊緣地圖之方法，以重建強化後的銳利影像，其中保留更精準的邊與平滑的紋理，亦同時應用至模糊軌跡的估計。接著，我們藉由影像衍生的補償技術，最佳化模糊軌跡的估計與清晰影像的還原。在清晰影像的還原步驟中，我們利用自動化閥值設定，二值化先前估計的模糊軌跡以保留正確的軌跡，進而還原成清晰的影像並抑止波紋干擾。
　　實驗結果將驗證我們的方法，相對於傳統從單一張模糊影像的去模糊化方法，是更快速且有效率的，並且，是更有可能還原成高品質與準確的影像，但帶有輕微的波紋。

摘要(英)

Capturing satisfactory photographs by using a hand-held camera are quite challenging even under correct camera settings, in other words, unsatisfactory photographs will usually be taken under image blurs. The reasons that cause image blur are generally resulted from camera shaking during exposure time which leads to inevitable important information loss and noise arisen.
　　Conventional blind deconvolution methods typically assume spacial/frequency domain constraints on images and/or parametric forms for the motion path of camera shaking for solving the ill-posed problem. The issues of current deblurring methods include reducing the common artifacts which are caused by image noise and/or errors in the estimated blur kernel while decreasing computation time. In this thesis, we present a novel blind deconvolution method which deblurs a single observed blurred image to produce a high-quality image. The purpose of our work is to suppress ringing artifacts effectively; moreover, to accelerate both blur kernel estimation and latent image restoration in iterative deblurring processes than conventional methods.
　　To reconstruct enhanced sharp image, we first apply some image pre-processing techniques and an edge mapping method to retain accurate edges and smooth regions from the estimated latent image, which will both join the later blur kernel estimation. Then, we formulate the optimization function by introducing penalty techniques with image derivatives to accelerate both the blur kernel estimation and latent image restoration. For latent image restoration, we also use auto-thresholding algorithm to truncate errors of the estimated kernel from the previous blur kernel estimation for recovering a sharp latent image while suppressing ringing artifacts.
　　Experimental results demonstrate that our proposed method is faster and more efficient than conventional methods for deblurring from a single blurred image. Moreover, high-quality images can be effectively and accurately produced with merely slight ringing effects.

關鍵字(中)

★ 盲蔽反捲積
★ 總變異
★ 清晰邊緣預測
★ 移動模糊
★ 去動態模糊

關鍵字(英)

★ Blind Deconvolution
★ Sharp Edge Prediction
★ Motion Deblurring
★ Total Variation

論文目次

TITLE PAGE (CHINESE)　I
TITLE PAGE (ENGLISH)　II
AUTHORITY PAGE　III
TESTIMONIAL　IV
SIGNATURE PAGE　V
ABSTRACT (CHINESE)　VI
ABSTRACT (ENGLISH)　VII
ACKNOWLEDGEMENTS　VIII
TABLE OF CONTENTS　IX
LIST OF FIGURES　XII
LIST OF TABLES　XIV
1　　INTRODUCTION　1
　　　1.1　SUMMARY OF APPROACH CHARACTERISTICS　2
　　　　　　PSF Estimation Base on Enhanced Sharp Image　2
　　　　　　Reduce Ringing Artifacts　3
　　　　　　Speed Up and Recover High-quality Deblurring Results　3
　　　1.2　ORGANIZATION OF THE DISSERTATION　3
2　　PREVIOUS WORK　4
　　　2.1　IMAGE BLUR OVERVIEW　7
　　　2.2　IMAGE BLUR MODEL　8
　　　2.3　IMAGE DECONVOLUTION OVERVIEW　9
　　　2.4　PSF ESTIMATION UNDER PSF ASSUMPTION　13
　　　　　　Motion Blur　14
　　　　　　Defocus Blur　15
　　　　　　Frequency Domain Zeros　15
　　　　　　Parameter Search　16
　　　2.5　PSF ESTIMATION UNDER IMAGE ASSUMPTION　17
　　　　　　Bayesian Blind-Deconvolution　17
　　　　　　Previous Work by using Image Priors　19
　　　　　　Natural Image Priors　21
　　　2.6　MULTI-IMAGE BLUR ESTIMATION　23
　　　2.7　SUMMARY　27
3　　FORMULATION OF BLIND DECONVOLUTION　28
4　　NUMERICAL ALGORITHM　32
　　　4.1　SHARP IMAGE RECONSTRUCTION　35
　　　　　　Suppress Noise　35
　　　　　　Enhance Sharp Edge　36
　　　　　　Edge Mapping　38
　　　4.2 BLUR KERNEL ESTIMATION　39
　　　　　　Auto-thresholding　41
　　　4.3 LATENT IMAGE RESTORATION　42
　　　　　　Convergent Theorem　45
　　　　　　Function Value　45
　　　　　　Signal-to-Noise Ratio　46
　　　4.4 UP/DOWN SAMPLING　48
5　　EXPERIMENTAL RESULTS　50
　　　5.1　SYNTHETIC IMAGES　50
　　　5.2　REAL IMAGES　53
6　　CONCLUSIONS AND FUTURE WORKS　54
　　　6.1　CONCLUSIONS　54
　　　6.2　FUTURE WORKS　54
BIBLIOGRAPHY　56

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指導教授

范國清(Kuo-Chin Fan)

審核日期

2010-7-14

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